{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-covid-19-pneumonia-severity-on","title":"Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning","arxiv_id":"2005.11856","date":"2020-05-24","proceeding":null,"authors":["Joseph Paul Cohen","Lan Dao","Paul Morrison","Karsten Roth","Yoshua Bengio","Beiyi Shen","Almas Abbasi","Mahsa Hoshmand-Kochi","Marzyeh Ghassemi","Haifang Li","Tim Q Duong"],"abstract":"Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods: Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results: This study finds that training a regression model on a subset of the outputs from an this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions: These results indicate that our model's ability to gauge severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the intensive care unit (ICU). A proper clinical trial is needed to evaluate efficacy. To enable this we make our code, labels, and data available online at https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-severity and https://github.com/ieee8023/covid-chestxray-dataset","url_abs":"https://arxiv.org/abs/2005.11856v3","url_pdf":"https://arxiv.org/pdf/2005.11856v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/mlmed/torchxrayvision","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/mlmed/covid-severity","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/Mithunjack/COVID-19-Xray-Image-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-severity","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/xinli0928/COVID-Xray","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"predicting-covid-19-pneumonia-severity-on","repo_url":"https://github.com/ieee8023/covid-chestxray-dataset/blob/master/annotations/covid-severity-scores.csv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}